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R Data Mining

You're reading from  R Data Mining

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Pages 442 pages
Edition 1st Edition
Languages
Concepts
Toc

Table of Contents (22) Chapters close

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Why to Choose R for Your Data Mining and Where to Start 2. A First Primer on Data Mining Analysing Your Bank Account Data 3. The Data Mining Process - CRISP-DM Methodology 4. Keeping the House Clean – The Data Mining Architecture 5. How to Address a Data Mining Problem – Data Cleaning and Validation 6. Looking into Your Data Eyes – Exploratory Data Analysis 7. Our First Guess – a Linear Regression 8. A Gentle Introduction to Model Performance Evaluation 9. Don't Give up – Power up Your Regression Including Multiple Variables 10. A Different Outlook to Problems with Classification Models 11. The Final Clash – Random Forests and Ensemble Learning 12. Looking for the Culprit – Text Data Mining with R 13. Sharing Your Stories with Your Stakeholders through R Markdown 14. Epilogue
15. Dealing with Dates, Relative Paths and Functions

Develop an R markdown report in RStudio


There are three kinds of basic contents of an R markdown document:

  • Text chunks
  • Inline R code
  • Code chunks

A brief introduction to markdown 

Now that you have deleted all of the content originally provided within the template, you actually find yourself in some kind of text field where it is possible to write down characters as in a common .txt file. To be more precise though, what you are going to write here will not be interpreted as common text but rather as Markdown code. Behind this name, there is a really convenient technical solution developed by John Gruber to provide people with a way to write in plain text documents that could lately be rendered as LaTeX, .pdf, or even .html

Basically, when you write in markdown, you don't have to worry about formatting or alignment anymore, since this will be eventually handled from subsequent rendering instruments, such as the rmarkdown functions in this case. All you have to worry about is specifying the headings...

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